This research is directed toward improved speech reception for users of hearing aids and cochlear implants through a program of research on models of speech intelligibility. The goal is to develop and experimentally evaluate a robust physical metric that predicts intelligibility scores for a variety of alterations of the speech signal for both hearing-impaired (HI) listeners and Cochlear Implant (Cl) users. This metric will be an adaptation of the speech-based Speech Transmission Index (sSTI) that computes a novel metric from the speech signals directly and the characteristics of individual listeners, and deals with a wide class of nonlinear distortions that are not adequately modeled by traditional STI methods. We have 4 aims. The first aim is to measure speech reception in 3 classes of listeners: HI listeners, Cl users, and normal-hearing subjects listening through a channel-vocoder simulation of cochlear-implant sound processing for 4 types of alterations of speech (acoustic degradations arising from noise and reverberation, band-pass filtering, amplitude compression, and noise-reduction algorithms). The second aim is to characterize the relevant basic psychoacoustic abilities of individual HI listeners and Cl users (in terms of basic sensitivity, dynamic range, spectral resolution, and temporal resolution) and their ability to integrate cues across different filtered bands of speech. The third aim is to develop sSTI-based metrics of speech intelligibility and apply them to the stimuli used to test HI and Cl listeners. The metrics will incorporate the individual listener characteristics, both psychoacoustic abilities and facility at integrating cues across frequency. The fourth aim is to evaluate these metrics by comparing metric predictions for a variety of listeners and speech processing conditions to the empirical data we have obtained on measures of speech reception. At the end of this project, we will have a single metric that is applicable to both HI listeners and Cl users (and by extension to normal-hearing listeners as well). This metric will predict the effects of noise, reverberation, filtering, amplitude compression, and noise reduction algorithms on speech intelligibility and will have a number of applications in research and clinical settings.